Retinal OCT Disease Classification
8 papers with code • 2 benchmarks • 2 datasets
Classifying different Retinal degeneration from Optical Coherence Tomography Images (OCT).
Benchmarks
These leaderboards are used to track progress in Retinal OCT Disease Classification
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Libraries
Use these libraries to find Retinal OCT Disease Classification models and implementations
OCTID
RETOUCH
Most implemented papers
Deep Residual Learning for Image Recognition
tensorflow/models
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CVPR 2016
Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.
MobileNetV2: Inverted Residuals and Linear Bottlenecks
tensorflow/models
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CVPR 2018
In this paper we describe a new mobile architecture, MobileNetV2, that improves the state of the art performance of mobile models on multiple tasks and benchmarks as well as across a spectrum of different model sizes.
Rethinking the Inception Architecture for Computer Vision
tensorflow/models
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CVPR 2016
Convolutional networks are at the core of most state-of-the-art computer vision solutions for a wide variety of tasks.
Optic-Net: A Novel Convolutional Neural Network for Diagnosis of Retinal Diseases from Optical Tomography Images
SharifAmit/OCT_Classification
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Diagnosing different retinal diseases from Spectral Domain Optical Coherence Tomography (SD-OCT) images is a challenging task.
Retinal OCT disease classification with variational autoencoder regularization
mlaves/oct-classification-vae-regularization
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A recent study established a diagnostic tool based on convolutional neural networks (CNN), which was trained on a large database of retinal OCT images.
Improving Robustness using Joint Attention Network For Detecting Retinal Degeneration From Optical Coherence Tomography Images
SharifAmit/Robust_Joint_Attention
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Noisy data and the similarity in the ocular appearances caused by different ophthalmic pathologies pose significant challenges for an automated expert system to accurately detect retinal diseases.
Matching the Clinical Reality: Accurate OCT-Based Diagnosis From Few Labels
Valentyn1997/oct-diagn-semi-supervised
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Furthermore, our experiments show that exponential moving average (EMA) of model parameters, which is a component of both algorithms, is not needed for our classification problem, as disabling it leaves the outcome unchanged.
TINC: Temporally Informed Non-Contrastive Learning for Disease Progression Modeling in Retinal OCT Volumes
EmreTaha/TINC
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Recent contrastive learning methods achieved state-of-the-art in low label regimes.
